Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Sustainable vitreoretinal surgery: balancing environmental responsibility with patient choice.

Eye (London, England)·2026

Same author

Pembrolizumab-associated choroidal inflammation on a background of treated choroidal melanoma.

BMJ case reports·2026

Same author

GWAS meta-analysis provides new insights into uveal melanoma risk.

British journal of cancer·2026

Same author

The complement system contributes to the immunosuppressive microenvironment of uveal melanoma.

Journal of translational medicine·2026

Same author

Vitreoretinal Surgery for Intraocular Complications Following Radiotherapy Treatment of Uveal Melanoma.

Cancers·2026

Same author

Modernising patient education in vitreoretinal surgery.

Eye (London, England)·2026

Search research articles

Related Experiment Video

Updated: Jun 3, 2025

Implantation and Evaluation of Melanoma in the Murine Choroid via Optical Coherence Tomography

Implantation and Evaluation of Melanoma in the Murine Choroid via Optical Coherence Tomography

Published on: December 2, 2022

Differentiating Choroidal Melanomas and Nevi Using a Self-Supervised Deep Learning Model Applied to Clinical

Max Jackson^1,2, Helen Kalirai^1,2, Rumana N Hussain^1,3

¹Liverpool Ocular Oncology Research Group, Department of Eye and Vision Science, Institute of Life Course and Medical Sciences (ILCaMS), University of Liverpool, Liverpool, United Kingdom.

Ophthalmology Science

|January 13, 2025

Summary

A deep learning model, RETFound, effectively differentiates uveal melanoma (UM) from nevi using fundus images. This validated self-supervised model shows high accuracy in classifying ocular conditions.

Keywords:

Choroidal melanoma Deep learning Foundation models Fundoscopy Nevi

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Related Experiment Videos

Last Updated: Jun 3, 2025

Implantation and Evaluation of Melanoma in the Murine Choroid via Optical Coherence Tomography

Implantation and Evaluation of Melanoma in the Murine Choroid via Optical Coherence Tomography

Published on: December 2, 2022

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Posterior uveal melanoma (UM) is a rare but serious ocular malignancy.
Accurate differentiation between UM and benign nevi is crucial for appropriate patient management.
Existing diagnostic methods can be limited, necessitating advanced analytical tools.

Purpose of the Study:

To evaluate the performance of RETFound, a self-supervised deep learning model, in distinguishing between uveal melanoma and nevi.
To assess the model's utility in classifying healthy eyes alongside UM and nevi.
To validate the model's accuracy on a large, single-center dataset.

Main Methods:

A case-control study utilizing ultrawidefield fundoscopy images (color and autofluorescence) from 4255 patients.
Analysis of 18,510 UM, 8,671 nevi, and 1,192 healthy eye images after quality exclusion.
Fine-tuning the RETFound deep learning model for binary (UM vs. nevi) and tertiary (UM vs. nevi vs. healthy) classification tasks.

Main Results:

The model achieved an AUROC of 0.90 and accuracy of 0.83 for binary classification (UM vs. nevi).
For tertiary classification, the model demonstrated a mean accuracy of 0.82 and an AUROC of 0.92.
These results indicate strong performance in differentiating ocular pathologies.

Conclusions:

Self-supervised deep learning models like RETFound are feasible for accurate differentiation of UM and nevi.
The model shows high accuracy in a large, imbalanced dataset from a single clinical center.
Further validation on external cohorts is planned to assess broader clinical applicability.